Time Varying Autoregressive (TVAR) models play a key role in various applications such as radar processing, aeronautics and speech processing. Nevertheless, tracking TVAR parameters may be difficult, especially when the process is disturbed by an additive white noise. In this paper, we suggest the use of a recursive Errors-In-Variables method to estimate the variances of the driving process and the additive noise and to track TVAR parameters. This method is based on a Newton-Raphson algorithm. A comparative study with EKF, UKF and CDKF is also proposed.
A recursive errors-in-variables method for tracking time varying autoregressive parameters from noisy observations / J. Petitjean; E. Grivel; R. Diversi; R. Guidorzi. - ELETTRONICO. - (2010), pp. 840-844. (Intervento presentato al convegno 18th European Signal Processing Conference (EUSIPCO 2010) tenutosi a Aalborg, Danimarca nel 23-27 Agosto 2010).
A recursive errors-in-variables method for tracking time varying autoregressive parameters from noisy observations
DIVERSI, ROBERTO;GUIDORZI, ROBERTO
2010
Abstract
Time Varying Autoregressive (TVAR) models play a key role in various applications such as radar processing, aeronautics and speech processing. Nevertheless, tracking TVAR parameters may be difficult, especially when the process is disturbed by an additive white noise. In this paper, we suggest the use of a recursive Errors-In-Variables method to estimate the variances of the driving process and the additive noise and to track TVAR parameters. This method is based on a Newton-Raphson algorithm. A comparative study with EKF, UKF and CDKF is also proposed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
